---
license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - weather
  - climate
  - regional
  - india
pretty_name: indibench_data
size_categories:
  - 1M<n<10M
---

# IndiBench: A Machine Learning-Ready Regional Forecasting Dataset over India

**IndiBench** provides a curated benchmark dataset for machine learning-based regional weather forecasting over the Indian subcontinent. It is built upon the **Indian Monsoon Data Assimilation and Analysis (IMDAA)** reanalysis dataset, produced under the National Monsoon Mission by NCMRWF, UK Met Office, and IMD.

---

## 🌏 Dataset Overview

The original IMDAA dataset is a high-resolution regional reanalysis developed by the **National Centre for Medium Range Weather Forecasting (NCMRWF)**, Ministry of Earth Sciences (MoES), Government of India, in collaboration with the **UK Met Office** and the **India Meteorological Department (IMD)**. It provides hourly weather data from **1979 to 2020** over the Indian subcontinent at a **0.12° (~12 km)** spatial resolution and includes over **57 variables** across **63 pressure levels**.

However, the raw dataset presents several challenges for machine learning workflows, including download difficulty, lack of standardized splits, and storage in meteorological formats. **IndiBench** addresses these limitations by offering a clean, ready-to-use subset for ML applications.

---

## 📦 Contents

IndiBench includes:
- **Time range**: 2000–2019 (20 years)
- **Interval**: 6-hourly (00, 06, 12, 18 UTC)
- **Region**: 6°N–36.72°N, 66.6°E–97.25°E (~256×256 grid)
- **Train/Val/Test splits**:
  - Train: 2000–2017 (~26,500 samples)
  - Val: 2018 (~1,500 samples)
  - Test: 2019 (~1,500 samples)
- **Variables**: 43 channels (see below)

---

## 📑 Variable List

| Category               | Variables                                                                 |
|------------------------|---------------------------------------------------------------------------|
| **Single-level**       | TMP (2m temp), UGRD/VGRD (10m wind), APCP (precip), PRMSL (MSLP), TCDCRO (cloud cover) |
| **Pressure-level**     | TMP_prl, HGT, UGRD_prl, VGRD_prl, RH — at 50, 250, 500, 600, 700, 850, 925 hPa |
| **Static fields**      | MTERH (terrain height), LAND (land cover)                                 |

---

## 💾 Data Formats

IndiBench is released in two formats:

### 🧪 Zarr Format
- Chunked, cloud-native array storage
- Compatible with `xarray`, `dask`
- Suitable for scientific analysis and fast slicing

```python
import xarray as xr
ds = xr.open_zarr("imdaa_bench_incremental.zarr", consolidated=True)
```

---

### 🚀 HDF5 Format
- Optimized for ML training
- Each `.h5` file = one time step with all variables
- Pre-split into `train/`, `val/`, and `test/`

```python
import h5py
f = h5py.File("imdaa_bench_h5/train/20010101_00.h5", "r")
print(list(f.keys()))
```

---

## 📜 License and Terms of Use

This dataset is released under the **Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0)** license.

- ✅ Free for non-commercial, educational, and research use
- ❌ For commercial use, contact: `director@ncmrwf.gov.in`
- 📧 Send a copy of any publication using this dataset to the same address

---

## 🔗 References

- [IMDAA Reanalysis Portal (NCMRWF)](https://rds.ncmrwf.gov.in/)
- [CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/)

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For questions, issues, or contributions, please open a Discussion or Issue on the Hugging Face page.